Jung, S. C. | Search bioRxiv

bioRxiv2020

Mendelian randomization identifies folliculin expression as a mediator of diabetic retinopathy.

The goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential gene expression in response to high glucose in lymphoblastoid cell lines derived from matched individuals with type 1 diabetes (T1D) with and without retinopathy. Those genes exhibiting the largest difference in glucose response between individuals with diabetes with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a genome-wide association study meta-analysis. All genetic variants associated with gene expression (expression Quantitative Trait Loci, eQTLs) of the glucose response genes were tested for association with diabetic retinopathy. We detected an enrichment of the eQTLs from the glucose response genes among small association p-values and identified folliculin (FLCN) as a susceptibility gene for diabetic retinopathy. We show that expression of FLCN in response to glucose was greater in individuals with diabetic retinopathy compared to individuals with diabetes without retinopathy. Three large, independent cohorts of individuals with diabetes revealed an association of FLCN eQTLs to diabetic retinopathy. Mendelian randomization further confirmed a direct positive effect of increased FLCN expression on retinopathy in individuals with diabetes. Together, our studies integrating genetic association and gene expression implicate FLCN as a disease gene for diabetic retinopathy.

genomics↗

bioRxiv2018

Reproducible big data science: A case study in continuous FAIRness

Big biomedical data create exciting opportunities for discovery, but make it difficult to capture analyses and outputs in forms that are findable, accessible, interoperable, and reusable (FAIR). In response, we describe tools that make it easy to capture, and assign identifiers to, data and code throughout the data lifecycle. We illustrate the use of these tools via a case study involving a multi-step analysis that creates an atlas of putative transcription factor binding sites from terabytes of ENCODE DNase I hypersensitive sites sequencing data. We show how the tools automate routine but complex tasks, capture analysis algorithms in understandable and reusable forms, and harness fast networks and powerful cloud computers to process data rapidly, all without sacrificing usability or reproducibility--thus ensuring that big data are not hard-to-(re)use data. We compare and contrast our approach with other approaches to big data analysis and reproducibility.

bioinformatics↗